OpenRaiser/Intern-Atlas huggingface dataset:
SGX-Aware Container Orchestration for Heterogeneous Clusters
Venue: arXiv cs.DC 2018
Year: 2018

Containers are becoming the de facto standard to package and deploy applications and micro-services in the cloud. Several cloud providers (e.g., Amazon, Google, Microsoft) begin to offer native support on their infrastructure by integrating container orchestration tools within their cloud offering. At the same time, the security guarantees that containers offer to applications remain questionable. Customers still need to trust their cloud provider with respect to data and code integrity. The recent introduction by Intel of Software Guard Extensions (SGX) into the mass market offers an alternative to developers, who can now execute their code in a hardware-secured environment without trusting the cloud provider. This paper provides insights regarding the support of SGX inside Kubernetes, an industry-standard container orchestrator. We present our contributions across the whole stack supporting execution of SGX-enabled containers. We provide details regarding the architecture of the scheduler and its monitoring framework, the underlying operating system support and the required kernel driver extensions. We evaluate our complete implementation on a private cluster using the real-world Google Borg traces. Our experiments highlight the performance trade-offs that will be encountered when deploying SGX-enabled micro-services in the cloud.
IoT Security: An End-to-End View and Case Study
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we present an end-to-end view of IoT security and privacy and a case study. Our contribution is three-fold. First, we present our end-to-end view of an IoT system and this view can guide risk assessment and design of an IoT system. We identify 10 basic IoT functionalities that are related to security and privacy. Based on this view, we systematically present security and privacy requirements in terms of IoT system, software, networking and big data analytics in the cloud. Second, using the end-to-end view of IoT security and privacy, we present a vulnerability analysis of the Edimax IP camera system. We are the first to exploit this system and have identified various attacks that can fully control all the cameras from the manufacturer. Our real-world experiments demonstrate the effectiveness of the discovered attacks and raise the alarms again for the IoT manufacturers. Third, such vulnerabilities found in the exploit of Edimax cameras and our previous exploit of Edimax smartplugs can lead to another wave of Mirai attacks, which can be either botnets or worm attacks. To systematically understand the damage of the Mirai malware, we model propagation of the Mirai and use the simulations to validate the modeling. The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.
Causal Reasoning for Algorithmic Fairness
Venue: arXiv cs.AI 2018
Year: 2018

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understanding of causal approaches, argue why causality is necessary for any approach that wishes to be fair, and give a detailed analysis of the many recent approaches to causality-based fairness.
Predictable Performance and Fairness Through Accurate Slowdown Estimation in Shared Main Memory Systems
Venue: arXiv cs.AR 2018
Year: 2018

This paper summarizes the ideas and key concepts in MISE (Memory Interference-induced Slowdown Estimation), which was published in HPCA 2013 [97], and examines the work's significance and future potential. Applications running concurrently on a multicore system interfere with each other at the main memory. This interference can slow down different applications differently. Accurately estimating the slowdown of each application in such a system can enable mechanisms that can enforce quality-of-service. While much prior work has focused on mitigating the performance degradation due to inter-application interference, there is little work on accurately estimating slowdown of individual applications in a multi-programmed environment. Our goal is to accurately estimate application slowdowns, towards providing predictable performance. To this end, we first build a simple Memory Interference-induced Slowdown Estimation (MISE) model, which accurately estimates slowdowns caused by memory interference. We then leverage our MISE model to develop two new memory scheduling schemes: 1) one that provides soft quality-of-service guarantees, and 2) another that explicitly attempts to minimize maximum slowdown (i.e., unfairness) in the system. Evaluations show that our techniques perform significantly better than state-of-the-art memory scheduling approaches to address the same problems. Our proposed model and techniques have enabled significant research in the development of accurate performance models [35, 59, 98, 110] and interference management mechanisms [66, 99, 100, 108, 119, 120].
Autonomous Vehicle Scheduling At Intersections Based On Production Line Technique
Venue: arXiv cs.OH 2018
Year: 2018

This thesis considers the problem of scheduling autonomous vehicles at intersections. A new system is proposed which is more efficient and could replace the recently introduced Autonomous Intersection Management (AIM) model. The proposed system is based on the production line technique. The environment of the intersection, vehicles position, speeds, and turning are specified and determined in advance. The goal of the proposed system is to eliminate vehicle collision and reduce the waiting time to cross the intersection. Three different patterns of traffic flow towards the intersection have been tested. The system requires less waiting time, compared to the other models, including the random case where the flow is unpredictable. The K-Nearest Neighbors (KNN) algorithm has been used to predict vehicles making a right turn at the intersection. The experimental results show there is no chance of collision inside the intersection using the proposed model; however, the system might require more space in the traffic lane for some specific traffic patterns.
Optical Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

We develop a novel optical neural network (ONN) framework which introduces a degree of scalar invariance to image classification estima- tion. Taking a hint from the human eye, which has higher resolution near the center of the retina, images are broken out into multiple levels of varying zoom based on a focal point. Each level is passed through an identical convolutional neural network (CNN) in a Siamese fashion, and the results are recombined to produce a high accuracy estimate of the object class. ONNs act as a wrapper around existing CNNs, and can thus be applied to many existing algorithms to produce notable accuracy improvements without having to change the underlying architecture.
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Venue: arXiv cs.LG 2018
Year: 2018

Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations. Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations. Structural equation models (SEMs) and structural vector autoregressive models (SVARMs) have well-documented merits in identifying even directed topologies of complex graphs; while SEMs capture contemporaneous causal dependencies among nodes, SVARMs further account for time-lagged influences. This paper develops algorithms that iterate between inferring directed graphs that "best" fit the data, and estimating the network processes at reduced computational complexity by leveraging tools related to Kalman smoothing. To further accommodate delay-sensitive applications, an online joint inference approach is put forth that even tracks time-evolving topologies. Furthermore, conditions for identifying the network topology given partial observations are specified. It is proved that the required number of observations for unique identification reduces significantly when the network structure is sparse. Numerical tests with synthetic as well as real datasets corroborate the effectiveness of the novel approach.
Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid
Venue: arXiv cs.CV 2018
Year: 2018

Because of the powerful learning capability of deep neural networks, counting performance via density map estimation has improved significantly during the past several years. However, it is still very challenging due to severe occlusion, large scale variations, and perspective distortion. Scale variations (from image to image) coupled with perspective distortion (within one image) result in huge scale changes of the object size. Earlier methods based on convolutional neural networks (CNN) typically did not handle this scale variation explicitly, until Hydra-CNN and MCNN. MCNN uses three columns, each with different filter sizes, to extract features at different scales. In this paper, in contrast to using filters of different sizes, we utilize an image pyramid to deal with scale variations. It is more effective and efficient to resize the input fed into the network, as compared to using larger filter sizes. Secondly, we adaptively fuse the predictions from different scales (using adaptively changing per-pixel weights), which makes our method adapt to scale changes within an image. The adaptive fusing is achieved by generating an across-scale attention map, which softly selects a suitable scale for each pixel, followed by a 1x1 convolution. Extensive experiments on three popular datasets show very compelling results.
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.
Critical rationalism and the search for standard (field-normalized) indicators in bibliometrics
Venue: arXiv cs.DL 2018
Year: 2018

Bibliometrics plays an increasingly important role in research evaluation. However, no gold standard exists for a set of reliable and valid (field-normalized) impact indicators in research evaluation. This opinion paper recommends that bibliometricians develop and analyze these impact indicators against the backdrop of Popper's critical rationalism. The studies critically investigating the indicators should publish the results in such a way that they can be included in meta-analyses. The results of meta-analyses give guidance on which indicators can then be part of a set of indicators used as standard in bibliometrics. The generation and continuous revision of the standard set could be handled by the International Society for Informetrics and Scientometrics (ISSI).
Client-side Straggler-Aware I/O Scheduler for Object-based Parallel File Systems
Venue: arXiv cs.DC 2018
Year: 2018

Object-based parallel file systems have emerged as promising storage solutions for high-performance computing (HPC) systems. Despite the fact that object storage provides a flexible interface, scheduling highly concurrent I/O requests that access a large number of objects still remains as a challenging problem, especially in the case when stragglers (storage servers that are significantly slower than others) exist in the system. An efficient I/O scheduler needs to avoid possible stragglers to achieve low latency and high throughput. In this paper, we introduce a log-assisted straggler-aware I/O scheduling to mitigate the impact of storage server stragglers. The contribution of this study is threefold. First, we introduce a client-side, log-assisted, straggler-aware I/O scheduler architecture to tackle the storage straggler issue in HPC systems. Second, we present three scheduling algorithms that can make efficient decision for scheduling I/Os while avoiding stragglers based on such an architecture. Third, we evaluate the proposed I/O scheduler using simulations, and the simulation results have confirmed the promise of the newly introduced straggler-aware I/O scheduler.
Investigating the Agility Bias in DNS Graph Mining
Venue: arXiv cs.CR 2018
Year: 2018

The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based graph theoretical data mining applications. By building on two conventional metrics for observing malicious DNS agility, the agility bias is observed by comparing bipartite DNS graphs to different subgraphs from which vertices and edges are removed according to two criteria. According to an empirical experiment with two longitudinal DNS datasets, irrespective of the criterion, the agility bias is observed to be severe particularly regarding the effect of outlying domains hosted and delivered via content delivery networks and cloud computing services. With these observations, the paper contributes to the research domains of cyber security and DNS mining. In a larger context of applied graph mining, the paper further elaborates the practical concerns related to the learning of large and dynamic bipartite graphs.
NFVactor: A Resilient NFV System using the Distributed Actor Model
Venue: arXiv cs.NI 2018
Year: 2018

Resilience functionality, including failure resilience and flow migration, is of pivotal importance in practical network function virtualization (NFV) systems. However, existing failure recovery procedures incur high packet processing delay due to heavyweight process-checkpointing, while flow migration has poor performance due to centralized control. This paper proposes NFVactor, a novel NFV system that aims to provide lightweight failure resilience and high-performance flow migration. NFVactor enables these by using actor model to provide a per-flow execution environment, so that each flow can replicate and migrate itself with improved parallelism, while the efficiency of the actor model is guaranteed by a carefully designed runtime system. Moreover, NFVactor achieves transparent resilience: once a new NF is implemented for NFVactor, the NF automatically acquires resilience support. Our evaluation result shows that NFVactor achieves 10Gbps packet processing, flow migration completion time that is 197 times faster than existing system, and packet processing delay stablized at around 20 microseconds during replication.
On the Semantics of Snapshot Isolation
Venue: arXiv cs.LO 2018
Year: 2018

Snapshot isolation (SI) is a standard transactional consistency model used in databases, distributed systems and software transactional memory (STM). Its semantics is formally defined both declaratively as an acyclicity axiom, and operationally as a concurrent algorithm with memory bearing timestamps. We develop two simpler equivalent operational definitions of SI as lock-based reference implementations that do not use timestamps. Our first locking implementation is prescient in that requires a priori knowledge of the data accessed by a transaction and carries out transactional writes eagerly (in-place). Our second implementation is non-prescient and performs transactional writes lazily by recording them in a local log and propagating them to memory at commit time. Whilst our first implementation is simpler and may be better suited for developing a program logic for SI transactions, our second implementation is more practical due to its non-prescience. We show that both implementations are sound and complete against the declarative SI specification and thus yield equivalent operational definitions for SI. We further consider, for the first time formally, the use of SI in a context with racy non-transactional accesses, as can arise in STM implementations of SI. We introduce robust snapshot isolation (RSI), an adaptation of SI with similar semantics and guarantees in this mixed setting. We present a declarative specification of RSI as an acyclicity axiom and analogously develop two operational models as lock-based reference implementations (one eager, one lazy). We show that these operational models are both sound and complete against the declarative RSI model.
Max-Product for Maximum Weight Matching - Revisited
Venue: arXiv cs.DS 2018
Year: 2018

We focus on belief propagation for the assignment problem, also known as the maximum weight bipartite matching problem. We provide a constructive proof that the well-known upper bound on the number of iterations (Bayati, Shah, Sharma 2008) is tight up to a factor of four. Furthermore, we investigate the behavior of belief propagation when convergence is not required. We show that the number of iterations required for a sharp approximation consumes a large portion of the convergence time. Finally, we propose an "approximate belief propagation" algorithm for the assignment problem.
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (Re-ID) algorithms. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose to establish the patch-wise correspondences of positive training pairs via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences can be obtained. To circumvent the cumbersome \emph{on-line} graph matching in testing phase, we propose to transfer the \emph{off-line} learned patch-wise correspondences from the positive training pairs to test pairs. In detail, for each test pair, the training pairs with similar pose-pair configurations are selected as references. The matching patterns (i.e., the correspondences) of the selected references are then utilized to calculate the patch-wise feature distances of this test pair. To enhance the robustness of correspondence transfer, we design a novel pose context descriptor to accurately model human body configurations, and present an approach to measure the similarity between a pair of pose context descriptors. Meanwhile, to improve testing efficiency, we propose a correspondence template ensemble method using the voting mechanism, which significantly reduces the amount of patch-wise matchings involved in distance calculation. With aforementioned strategies, the REGCT model can effectively and efficiently handle the spatial misalignment problem in Re-ID. Extensive experiments on five challenging benchmarks, including VIPeR, Road, PRID450S, 3DPES and CUHK01, evidence the superior performance of REGCT over other state-of-the-art approaches.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Venue: arXiv cs.CV 2018
Year: 2018

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells. For systematic recognition and recovery of macromolecular structures captured by CECT, methods for several important tasks such as subtomogram classification and semantic segmentation have been developed. However, the recognition and recovery of macromolecular structures are still very difficult due to high molecular structural diversity, crowding molecular environment, and the imaging limitations of CECT. In this paper, we propose a novel multi-task 3D convolutional neural network model for simultaneous classification, segmentation, and coarse structural recovery of macromolecules of interest in subtomograms. In our model, the learned image features of one task are shared and thereby mutually reinforce the learning of other tasks. Evaluated on realistically simulated and experimental CECT data, our multi-task learning model outperformed all single-task learning methods for classification and segmentation. In addition, we demonstrate that our model can generalize to discover, segment and recover novel structures that do not exist in the training data.
#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter
Venue: arXiv cs.CL 2018
Year: 2018

Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific cohorts, identifying posts mentioning intake of medicine by the user is necessary. Towards this objective, we train different deep neural network classification models on a publicly available annotated dataset and study their performances on identifying mentions of personal intake of medicine in tweets. We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles. We use random search for tuning the hyperparameters of the models and share the details of the values taken by the hyperparameters for the best learnt model in different deep neural network architectures. Our system produces state-of-the-art results, with a micro- averaged F-score of 0.693.